Fig. 1. Overall architecture of our network. (a) Training network; (b) network prediction process; (c) structure of the SE residual module

Fig. 2. Algorithm performance under images with different sizes, densities, and SNRs. (a) F1 under different sizes; (b) F1 under different densities; (c) F1 under different SNRs; (d) localization error under different sizes; (e) localization error under different densities; (f) localization error under different SNRs

Fig. 3. Prediction for high-density spot images. (a) Fluorescent spot prediction reaching the diffraction limit; (b) prediction based on density image; (c) detection results of each algorithm in the case of 1200 spots per image

Fig. 4. Representative images of datasets and their corresponding prediction

Table 1. Performance of SE-Res-UNet using different density map loss functions

Table 2. Performance of different models on vesicle dataset

Table 3. Detection and localization performances of different algorithms under different SNRs

Table 4. Detection and localization performances of different algorithms under different densities

Table 5. Detection and localization performances of different algorithms on different datasets